KDD2019 Research Track Session RT15: Mining in Emerging Applications II

[まとめページへのリンク](https://qiita.com/kotaaaa/items/4f144d660d504cc091cf)

#Research Track Session RT15: Mining in Emerging Applications II – Summit 4, Ground Level, Egan Center
Chair: Petko Bogdanov

## Optimizing Impression Counts for Outdoor Advertising
Yipeng Zhang (RMIT University); Yuchen Li (Singapore Management University ); Zhifeng Bao (RMIT University); Songsong Mo
(Wuhan University & RMIT University); Ping Zhang (Huawei)

How a billboard impress an audience?
Influence Measurement 
The logistic function (Advertising market and Customer behavior)
The effectiveness of advertisement repetition varies from one person to another.

Influence meautrement is not submodular 

- no approximation ratio for a greedy-based algorithm

NP-hard to approximate within any constant factor 

Upper-bound estimation 
Branch-and-Branch Framework

##### optimization 
BBS: branch-and-Bound Framework
PBBS: Branch-and-Bound Framework with Progressive Bound-Estimation

Data: trajectory dataset: NYC, LA
experiment: algorithm
greedy,top-k, BBS, PBBS, LazyProbe
Varying the budget B
Varying the number of Trajectories
Scalability test in NYC
Comparison with LazyProbe

##### Conclusion 

- real problem
- meet more than one billboard in each travel 
- not uniform cost od billboards 
- budget 
- real solution 
- while having the approximation guarantee
- real-world trajectory dataset and billboard


## Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms
Boyang Li (Northeastern University); Yurong Cheng (Beijing Institute of Technology); Ye Yuan (Northeastern University); Guoren
Wang (Beijing Institute of Technology); Lei Chen (The Hong Kong University of Science and Technology)

No talk

## Hidden POI Ranking with Spatial Crowdsourcing
Yue Cui (University of Electronic Science and Technology of China); Liwei Deng (University of Electronic Science and Technology
of China); Yan Zhao (Soochow University); Bin Yao (Shanghai Jiao Tong University); Vincent W. Zheng (WeBank); Kai Zheng
(University of Electronic Science and Technology of China)


Inequity of business market 
how to generate candidate tasks and distribute proper tasks to proper workers with a goal of aggregating an accurate ranking got comparable H-POI

##### H-POI ranking consideration 

- Maintain effectiveness
- worker quality
- task generation 
- Improve efficiency 
- Budget
- Time

Hidden POI ranking with spatial crowdsourcing 

##### Preview: key Definitions 
- spatial task
- comparable H-POI
- Valid Task Set(VTS)

Minimum VTS (MinVTS) Greedy Search Algorithm
Find MinVTS
Worker reliability 
category reliability 
Adapted from metapath2vec
Three meta paths are designed on check-in graph
area reliability 
adapted from X-means: AIC BIC based X-means clustering 

- adaptive number of clusters can be selected 
- simplify reliability calculation in an internal based approach 

Ranking aggregation 
main purpose: Predict a gold-standard ranking that hinges on combining  pairwise comparisons via crowdsourcing 

closely related work: crowdBT(Chen et al.)
Tree-constrained Skip (TCS) Search 
construct a minimum spanning trees(MSTs)

with H-POI ranking supervised(TCSS)
information gain

data: yelp-dataset 
Effect of sampling number
Effect of k(kappa)
Effect of H-POI number

##### Conclusion 

- Analyzed the necessities of H-PUI exploration
- Proposed That can aggregate H-POI ranking from pairwise comparison of the crowd
- The proposed approach can greatly reduce text pair searching time cost


## Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications
Zhe Jiang (University of Alabama); Arpan Man Sainju (University of Alabama)

Spatial structured prediction

- structured prediction  
 - syntax tree in linguistics, music note sequence
- spatial structured prediction 
- terrain map in hydrology
- potential energy landscape in material science 

Data-Driven Approach: The Fourth Paradigm of Scientific Discovery
The pitfalls of Data-driven approach

- Generalizability 
- Interpretability 
- Reproducibility 
- scalability 
- Limited ground truth 

flood mapping in Disaster Response
flood mapping in Hydrology 
National water forecasting 
In: spatial raster framework 
Out: Aspacial classification model 
Objective: Minimize classification errors
Constraints: explanatory feature layers contain noise 

challenges

- noise obstacles in imagery > cloud, shadows, tree canopies 
- special structure on 3D surface
- large data volumes

##### related works

- spatial proximity
  - MRF, CRF, SAR
  - Gaussian process(Kriging) 
  - CNN, Embedding 
spartial network 
  - network kringing
- geometric DL
- Graph DL


Contour Tree on 3D surface 
Contour tree (poly-tree structure)
Collapsed contour tree
Hidden Markov contour tree
Hidden Markov Contour Tree(HMCT)

- probabilistic graphical model
- Assumptions:
- feature is Gaussian 
- class transitional probability 
- HMCT: Parameter Learning 
- Approach: EM algorithm 

experimental setup 

- data: Hurricane Mathew flood, NC, 2016
- two study areas: Greenville NC, Grimesland NC

##### conclusion 

- HCMT model
- contour tree construction, parameter learning, class inference algorithm 


## Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta-Learning
Zheyi Pan (Shanghai Jiaotong University); Yuxuan Liang (Xidian University); Weifeng Wang (Shanghai Jiaotong University); Yong
Yu (Shanghai Jiaotong University); Yu Zheng (JD Intelligent Cities Research); Junbo Zhang (JD Intelligent Cities Research)

#####Intro

Important for 
trafic management 
public risk assesment 
public safety

##### challenge 
- spartial correlations 
- temporal correlations

##### challenge diversity of Spatio-temporal correlations 
- characteristic of locations and their mutual relationship are diverse 
- conventional method: ARIMA, GBRTm linear model 
- deep model 
 - spatial network temporal network to model ST correlations
 - use a single model to predict traffic on all location 



##### Insights
build a geo graph to describe spatial structure 
Node: locations
Edges relation between locations
geographical features reveal characteristics of nodes and edges & impact different types of ST correlations 

##### Overview od ST-MetaNet

- Encoder-Decoder
- meta graph Attention network 
- meta recurrent network
- meta knowledge learner 
- meta learner

##### meta graph attention

- calculate attention scores
- get meta knowledge about edge 
- weight generation 
- softmax&linear combination 

##### Meta gated recurrent unit 

##### evaluation
- data: TaxiBJ, METR-LA
- Metrics: MAE & RMSE
- use much less number of parameters 
- evaluation on meta networks 
- evaluation on meta knowledge

validate that meta-knowledge can reveal the similarity of ST corrections on nodes


##### conclusion 
- deep meta learning based framework for Spatio-temporal data
- meta graph attention for modeling diverse spatial correlations
- meta gated recurrent Unit for modeling diverse temporal correlations
- achieve significant improvement on two real-world traffic prediction task

## Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network
Junchen Ye (Beihang University); Leilei Sun (Beihang University); Bowen Du (Beihang University); Yanjie Fu (University of Central
Florida); Xinran Tong (Beihang University); Hui Xiong (Rutgers University)



##### background

- sharing transportation plays important roles
- pickup demand 

related-work

- hand-designed features
- LSTM/ConvLSTM + CNN

##### summary 

- employ the model spatio + temporal 
- USE CNN directly 
- Single transport to predict single transport


from a Micro View

- Bases Decomposing 
- wavelet Tranform

Base decomposition 

- decomposing methods 
 - clustering 
 - linear decomposing 

- proposed: autoencoder(applied)

Heterogeneous Information Fusion 

- In fact, there are some deep correlations between different transportation


extract the hidden correlation between different 
transport to improve the prediction accuracy 


##### Our method: Cost-net

- transportation pattern decomposing 
- heterogeneous information fusions
- spatial autoencoder & heterogenious Information Fusion 

##### Experiment

- Data: NYC Citi Bike, NYC taxi
- Timeshift: small size of a dataset will hurt the high-level features learning for autoencoder.
- cut original data with, all-time shifts expanse the dataset to train an autoencoder 
- not only expanse the dataset but, help to learn the traffic feature

##### How to evaluate methods?

- best/mean result? No
- In the box plot, our method has the best mean perform and smallest variance


a channel single to single, two-channel double to double and our method is four-channel 
the more channels we employ, the better results we get